1. Technical Field
The present disclosure relates to tagging signals and more specifically to tagging voice signals of interest in time variant data.
2. Introduction
Currently, there are a number of different models that are used to identify a signal of interest from a set of data. Typically, an analysis tool analyzes the output of a signal classifier over a period of time until the tool has enough information to identify a signal of interest. There are several well known forms of analysis that presently perform this function. Some examples are moving averages, least squares, convolution, and the Savitzky-Golay smoothing filter. However, these methods provide smoothing of the signal with respect to a measured change in time. Using time difference as a basis for smoothing has some consequences, including the fact that when the signal is interrupted, the interruption can cause disruption in the system resulting in reduced smoothing values. These systems have further problems including the inability to properly identify signals of interest when there are multiple signals available and only one is of interest. Also, these approaches generally require pre-segmented data and multiple passes over the data to generate an accurate identification.